Assignment - Business Intelligence - Programming
Paper Section 1: Reflection and Literature ReviewUsing Microsoft Word and Professional APA format, prepare a professional written paper supported with three sources of research that details what you have learned from chapters 3 and 4. This section of the paper should be a minimum of two pages. Paper Section 2: Applied Learning ExercisesIn this section of the professional paper, apply what you have learned from chapters 3 and 4 to descriptively address and answer the problems below. Important Note: Dot not type the actual written problems within the paper itself.Research some data warehouse vendors and obtain information about their products. Give special attention to vendors that provide tools for multiple purposes, such as Cognos, Software A&G, SAS Institute, and Oracle. Free online demos are available from some of these vendors. Download a demo or two and try them and describe your learning experience and review of this products.Download an information visualization tool, such as Tableau, QlikView, or Spotfire. If your school does not have an educational agreement with these companies, then a trial version would be sufficient for this exercise. Use your own data (if you have any) or use one of the data sets that comes with the tool (they usually have one or more data sets for demonstration purposes). Study the data, come up with a couple of business problems, and use data and visualization to analyze, visualize, and potentially solve those problems.Important Note: With limited time for a college class, perfection is not expected but effort to be exposed to various tools with attempts to learn about them is critical when considering a career in information technology associated disciplines.Important Note: There is no specific page requirement for this section of the paper but make sure any content provided fully addresses each problem.Paper Section 3: ConclusionsAfter addressing the problems, conclude your paper with details on how you will use this knowledge and skills to support your professional and or academic goals. This section of the paper should be around one page including a custom and original process flow or flow diagram to visually represent how you will apply this knowledge going forward. This customized and original flow process flow or flow diagram can be created using the “Smart Art” tools in Microsoft Word.Paper Section 4: APA Reference PageThe three or more sources of research used to support this overall paper should be included in proper APA format in the final section of the paper.Paper Review and Preparation to submit for GradingPlease make sure to proof read your post prior to submission. This professional paper should be well written and free of grammatical or typographical errors. Also remember not to plagiarize!!!!!!!!!!!
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BUSINESS INTELLIGENCE
AND ANALYTICS
RAMESH SHARDA
DURSUN DELEN
EFRAIM TURBAN
TENTH EDITION
.•
TENTH EDITION
BUSINESS INTELLIGENCE
AND ANALYTICS:
SYSTEMS FOR DECISION SUPPORT
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
With contributions by
J.E.Aronson
Tbe University of Georgia
Ting-Peng Liang
National Sun Yat-sen University
David King
]DA Software Group, Inc.
PEARSON
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Library of Congress Cataloging-in-Publication Data
Turban, Efraim.
[Decision support and expert system,)
Business intelligence and analytics: systems for decision support/Ramesh Sharda, Oklahoma State University,
Dursun Delen, Oklahoma State University, Efraim Turban, University of Hawaii; With contributions
by J. E. Aronson, The University of Georgia, Ting-Peng Liang, National Sun Yat-sen University,
David King, JOA Software Group, Inc.-Tenth edition.
pages cm
ISBN-13: 978-0-13-305090-5
ISBN-10: 0-13-305090-4
1. Management-Data processing. 2. Decision support systems. 3. Expert systems (Compute r science)
4. Business intelligence. I. Title.
HD30.2.T87 2014
658.403801 l-dc23
2013028826
10 9 8 7 6 5 4 3 2 1
PEARSON
ISBN 10: 0-13-305090-4
ISBN 13: 978-0-13-305090-5
BRIEF CONTENTS
Preface xxi
About the Authors xxix
PART I
Decision Making and Analytics: An Overview
PART II
1
Chapter 1
An Overview of Business Intelligence, Analytics,
and Decision Support 2
Chapter 2
Foundations and Technologies for Decision Making
Descriptive Analytics
77
Chapter 3
Data Warehousing
Chapter 4
Business Reporting, Visual Analytics, and Business
Performance Management 135
PART Ill Predictive Analytics
78
185
Chapter 5
Data Mining
Chapter 6
Techniques for Predictive Modeling
Chapter 7
Text Analytics, Text Mining, and Sentiment Analysis
Chapter 8
Web Analytics, Web Mining, and Social Analytics
186
PART IV Prescriptive Analytics
Chapter 9
37
243
288
338
391
Model-Based Decision Making: Optimization and MultiCriteria Systems 392
Chapter 10 Modeling and Analysis: Heuristic Search Methods and
Simulation 435
Chapter 11
Automated Decision Systems and Expert Systems
469
Chapter 12
Knowledge Management and Collaborative Systems
507
PART V Big Data and Future Directions for Business
Analytics 541
Chapter 13 Big Data and Analytics
542
Chapter 14 Business Analytics: Emerging Trends and Future
Impacts 592
Glossary
Index
634
648
iii
CONTENTS
Preface
xxi
About the Authors xxix
Part I
Decision Making and Analytics: An Overview
1
Chapter 1 An Overview of Business Intelligence, Analytics, and
Decision Support 2
1.1
Opening Vignette: Magpie Sensing Employs Analytics to
Manage a Vaccine Supply Chain Effectively and Safely 3
1.2
Changing Business Environments and Computerized
Decision Support 5
The Business Pressures-Responses-Support Model
1.3
Managerial Decision Making
The Nature of Managers Work
The Decision-Making Process
5
7
7
8
1.4
Information Systems Support for Decision Making
1.5
An Early Framework for Computerized Decision
Support 11
The Gorry and Scott-Morton Classical Framework
Computer Support for Structured Decisions
Computer Support for Semistructured Problems
13
13
The Concept of Decision Support Systems (DSS)
DSS as an Umbrella Term
14
A Framework for Business Intelligence (Bl)
Definitions of Bl
14
14
A Brief History of Bl
14
The Architecture of Bl
Styles of Bl
13
13
Evolution of DSS into Business Intelligence
1.7
11
12
Computer Support for Unstructured Decisions
1.6
9
15
15
The Origins and Drivers of Bl
16
A Multimedia Exercise in Business Intelligence 16
~ APPLICATION CASE 1.1 Sabre Helps Its Clients Through Dashboards
and Analytics 17
The DSS-BI Connection
1.8
18
Business Analytics Overview
Descriptive Analytics
~
20
APPLICATION CASE 1.2 Eliminating Inefficiencies at Seattle
Childrens Hospital
~
21
APPLICATION CASE 1.3 Analysis at the Speed of Thought
Predictive Analytics
iv
19
22
22
Conte nts
~
APPLICATION CASE 1.4 Moneybal/: Analytics in Sports and Movies
~
APPLICATION CASE 1.5 Analyzing Athletic Injuries
Prescriptive Analytics
23
24
24
~ APPLICATION CASE 1.6 Industrial and Commercial Bank of China
(ICBC) Employs Models to Reconfigure Its Branch Network
1.9
Analytics Applied to Different Domains 26
Analytics or Data Science? 26
Brief Introduction to Big Data Analytics
What Is Big Data? 27
~
25
27
APPLICATION CASE 1.7 Gilt Groupes Flash Sales Streamlined by Big
Data Analytics 29
1.10 Plan of the Book 29
Part I: Business Analytics: An Overview
Part II: Descriptive Analytics 30
29
Part Ill: Predictive Analytics 30
Part IV: Prescriptive Analytics 31
Part V: Big Data and Future Directions for Business Analytics 31
1.11 Resources, Links, and the Teradata University Network
Connection 31
Resources and Links 31
Vendors, Products, and Demos 31
Periodicals 31
The Teradata University Network Connection
The Books Web Site 32
Chapter Highlights
32
Questions for Discussion
~
•
Key Terms
33
•
32
33
Exercises
33
END-OF-CHAPTER APPLICATION CASE Nationwide Insurance Used Bl
to Enhance Customer Service 34
References
35
Chapter 2 Foundations and Technologies for Decision Making
2.1
2.2
Opening Vignette: Decision Modeling at HP Using
Spreadsheets 38
Decision Making: Introduction and Definitions 40
Characteristics of Decision Making 40
A Working Definition of Decision Making
Decision-Making Disciplines 41
2.3
2.4
41
Decision Style and Decision Makers 41
Phases of the Decision-Making Process 42
Decision Making: The Intelligence Phase 44
Problem (or Opportunity) Identification 45
~
APPLICATION CASE 2.1 Making Elevators Go Faster!
Problem Classification
46
Problem Decomposition
Problem Ownership
46
46
45
37
v
vi
Contents
2.5
Decision Making: The Design Phase
Models
47
Mathematical (Quantitative) Models
The Benefits of Models
Normative Models
Suboptimization
47
47
Selection of a Principle of Choice
48
49
49
Descriptive Models
50
Good Enough, or Satisficing
51
Developing (Generating) Alternatives
Measuring Outcomes
Risk
47
52
53
53
Scenarios
54
Possible Scenarios
54
Errors in Decision Making
54
2.6
Decision Making: The Choice Phase
2.7
Decision Making: The Implementation Phase
2.8
How Decisions Are Supported
Support for the Intelligence Phase
Support for the Design Phase
57
Support for the Choice Phase
58
56
58
Decision Support Systems: Capabilities
A DSS Application
55
56
Support for the Implementation Phase
2.9
55
59
59
2.10 DSS Classifications
61
The AIS SIGDSS Classification for DSS
Other DSS Categories
61
63
Custom-Made Systems Versus Ready-Made Systems
63
2.11 Components of Decision Support Systems
The Data Management Subsystem
64
65
The Model Management Subsystem 65
~ APPLICATION CASE 2.2 Station Casinos Wins by Building Customer
Relationships Using Its Data
~
66
APPLICATION CASE 2.3 SNAP DSS Helps OneNet Make
Telecommunications Rate Decisions 68
The User Interface Subsystem
68
The Knowledge-Based Management Subsystem 69
~ APPLICATION CASE 2.4 From a Game Winner to a Doctor!
Chapter Highlights
72
Questions for Discussion
~
•
Key Terms
73
•
70
73
Exercises
74
END-OF-CHAPTER APPLICATION CASE Logistics Optimization in a
Major Shipping Company (CSAV)
References
75
74
Conte nts
Part II Descriptive Analytics
Chapter 3 Data Warehousing
77
78
3.1
Opening Vignette: Isle of Capri Casinos Is Winning with
Enterprise Data Warehouse 79
3.2
Data Warehousing Definitions and Concepts
What Is a Data Warehouse?
81
A Historical Perspective to Data Warehousing
Characteristics of Data Warehousing
Data Marts
85
APPLICATION CASE 3.1 A Better Data Plan: Well-Established TELCOs
Leverage Data Warehousing and Analytics to Stay on Top in a
Competitive Industry 85
Data Warehousing Process Overview
~
3.4
83
84
Enterprise Data Warehouses (EDW)
Metadata 85
3.3
81
84
Operational Data Stores
~
Data Warehousing Architectures
Which Architecture Is the Best?
90
93
96
Data Integration and the Extraction, Transformation, and
Load (ETL) Processes 97
Data Integration
~
98
APPLICATION CASE 3.3 BP Lubricants Achieves BIGS Success
Extraction, Transfonnation, and Load
3.6
87
APPLICATION CASE 3.2 Data Warehousing Helps MultiCare Save
More Lives 88
Alternative Data Warehousing Architectures
3.5
102
APPLICATION CASE 3.4 Things Go Better with Cokes Data
Warehouse
103
Data Warehouse Development Approaches
~
103
APPLICATION CASE 3.5 Starwood Hotels & Resorts Manages Hotel
Profitability with Data Warehousing 106
Additional Data Warehouse Development Considerations
Representation of Data in Data Warehouse
Analysis of Data in the Data Warehouse
OLAP Versus OLTP
OLAP Operations
109
110
11 0
Real-Time Data Warehousing
~
113
APPLICATION CASE 3.6 EDW Helps Connect State Agencies in
Michigan 115
Massive Data Warehouses and Scalability
3.8
107
108
Data Warehousing Implementation Issues
~
98
100
Data Warehouse Development
~
3.7
81
116
117
APPLICATION CASE 3.7 Egg Pie Fries the Competition in Near Real
Time 118
vii
viii
Contents
3.9
Data Warehouse Administration, Security Issues, and Future
Trends 121
The Future of Data Warehousing
123
3.10 Resources, Links, and the Teradata University Network
Connection 126
Resources and Links 126
Cases 126
Vendors, Products, and Demos 127
Periodicals 127
Additional References 127
The Teradata University Network (TUN) Connection 127
Chapter Highlights
128
•
Questions for Discussion
Key Terms
128
•
128
Exercises
129
.... END-OF-CHAPTER APPLICATION CASE Continental Airlines Flies High
with Its Real-Time Data Warehouse
References
131
132
Chapter 4 Business Reporting, Visual Analytics, and Business
Performance Management 135
4.1
Opening Vignette:Self-Service Reporting Environment
Saves Millions for Corporate Customers 136
4.2
Business Reporting Definitions and Concepts
What Is a Business Report?
139
140
..,. APPLICATION CASE 4.1 Delta Lloyd Group Ensures Accuracy and
Efficiency in Financial Reporting
141
Components of the Business Reporting System
143
.... APPLICATION CASE 4.2 Flood of Paper Ends at FEMA
4.3
Data and Information Visualization
144
145
..,. APPLICATION CASE 4.3 Tableau Saves Blastrac Thousands of Dollars
with Simplified Information Sharing
A Brief History of Data Visualization
146
147
.... APPLICATION CASE 4.4 TIBCO Spotfire Provides Dana-Farber Cancer
Institute with Unprecedented Insight into Cancer Vaccine Clinical
Trials 149
4.4
Different Types of Charts and Graphs
Basic Charts and Graphs
Specialized Charts and Graphs
4.5
151
The Emergence of Data Visualization and Visual
Analytics 154
Visual Analytics
156
High-Powered Visual Analytics Environments
4.6
150
150
Performance Dashboards
158
160
.... APPLICATION CASE 4.5 Dallas Cowboys Score Big with Tableau and
Teknion
161
Conte nts
Dashboard Design
~
162
APPLICATION CASE 4.6 Saudi Telecom Company Excels with
Information Visualization 163
What to Look For in a Dashboard
164
Best Practices in Dashboard Design
165
Benchmark Key Performance Indicators with Industry Standards
Wrap the Dashboard Metrics with Contextual Metadata
165
Validate the Dashboard Design by a Usability Specialist
165
Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard
Enrich Dashboard with Business Users Comments
Present Information in Three Different Levels
4.7
166
~
4.8
166
167
APPLICATION CASE 4.7 IBM Cognos Express Helps Mace for Faster
and Better Business Reporting 169
Performance Measurement
Key Performance Indicator (KPI)
170
171
Performance Measurement System
4.9
166
166
Business Performance Management
Closed-Loop BPM Cycle
165
165
Pick the Right Visual Construct Using Dashboard Design Principles
Provide for Guided Analytics
165
Balanced Scorecards
The Four Perspectives
172
172
173
The Meaning of Balance in BSC
17 4
Dashboards Versus Scorecards
174
4.10 Six Sigma as a Performance Measurement System
The DMAIC Performance Model
175
176
Balanced Scorecard Versus Six Sigma
176
Effective Performance Measurement 177
~ APPLICATION CASE 4.8 Expedia.coms Customer Satisfaction
Scorecard
178
Chapter Highlights
179
Questions for Discussion
~
•
180
Exercises
181
184
Part Ill Predictive Analytics
Chapter 5 Data Mining
5.2
181
Key Terms
END-OF-CHAPTER APPLICATION CASE Smart Business Reporting
Helps Healthcare Providers Deliver Better Care 182
References
5.1
•
185
186
Opening Vignette: Cabelas Reels in More Customers with
Advanced Analytics and Data Mining 187
Data Mining Concepts and Applications
~
189
APPLICATION CASE 5.1 Smarter Insurance: Infinity P&C Improves
Customer Service and Combats Fraud with Predictive Analytics
191
ix
x
Contents
Definitions, Characteristics, and Benefits
192
..,. APPLICATION CASE 5.2 Harnessing Analytics to Combat Crime:
Predictive Analytics Helps Memphis Police Department Pinpoint Crime
and Focus Police Resources 196
5.3
How Data Mining Works 197
Data Mining Versus Statistics 200
Data Mining Applications 201
.... APPLICATION CASE 5.3 A Mine on Terrorist Funding
5.4
203
Data Mining Process 204
Step 1: Business Understanding 205
Step 2: Data Understanding 205
Step 3: Data Preparation 206
Step 4: Model Building 208
.... APPLICATION CASE 5.4 Data Mining in Cancer Research
Step 5: Testing and Evaluation
5.5
5.6
5.7
210
211
Step 6: Deployment 211
Other Data Mining Standardized Processes and Methodologies 212
Data Mining Methods 214
Classification 214
Estimating the True Accuracy of Classification Models 215
Cluster Analysis for Data Mining 220
..,. APPLICATION CASE 5.5 2degrees Gets a 1275 Percent Boost in Churn
Identification 221
Association Rule Mining 224
Data Mining Software Tools 228
.... APPLICATION CASE 5.6 Data Mining Goes to Hollywood: Predicting
Financial Success of Movies 231
Data Mining Privacy Issues, Myths, and Blunders 234
Data Mining and Privacy Issues 234
.... APPLICATION CASE 5.7 Predicting Customer Buying Patterns-The
Target Story 235
Data Mining Myths and Blunders 236
Chapter Highlights
237
•
Key Terms
238
Questions for Discussion 238 • Exercises 239
.... END-OF-CHAPTER APPLICATION CASE Macys.com Enhances Its
Customers Shopping Experience with Analytics
References
241
241
Chapter 6 Techniques for Predictive Modeling
243
6.1
Opening Vignette: Predictive Modeling Helps Better
Understand and Manage Complex Medical
Procedures 244
6.2
Basic Concepts of Neural Networks 247
Biological and Artificial Neural Networks 248
..,. APPLICATION CASE 6.1 Neural Networks Are Helping to Save Lives in
the Mining Industry 250
Elements of ANN 251
Conte nts
Network Information Processing 252
Neural Network Architectures 254
~
APPLICATION CASE 6.2 Predictive Modeling Is Powering the Power
Generators 256
6.3
Developing Neural Network-Based Systems
The General ANN Learning Process 259
Backpropagation 260
6.4
Illuminating the Black Box of ANN with Sensitivity
Analysis 262
~
6.5
APPLICATION CASE 6.3 Sensitivity Analysis Reveals Injury Severity
Factors in Traffic Accidents 264
Support Vector Machines
~
265
APPLICATION CASE 6.4 Managing Student Retention with Predictive
Modeling 266
Mathematical Formulation of SVMs
Primal Form 271
Dual Form 271
Soft Margin 271
Nonlinear Classification
Kernel Trick 272
270
272
6.6
A Process-Based Approach to the Use of SVM
Support Vector Machines Versus Artificial Neural Networks
6.7
Nearest Neighbor Method for Prediction
Similarity Measure: The Distance Metric 276
Parameter Selection
~
258
273
274
275
277
APPLICATION CASE 6.5 Efficient Image Recognition and
Categorization with kNN 278
Chapter Highlights
280
•
Key Terms
280
Questions for Discussion 281 • Exercises 281
~ END-OF-CHAPTER APPLICATION CASE Coors Improves Beer Flavors
with Neural Networks
References
284
285
Chapter 7 Text Analytics, Text Mining, and Sentiment Analysis
288
7.1
Opening Vignette: Machine Versus Men on Jeopardy!: The
Story of Watson 289
7.2
Text Analytics and Text Mining Concepts and
Definitions 291
~
7.3
Natural Language Processing
~
7.4
APPLICATION CASE 7.1 Text Mining for Patent Analysis
296
APPLICATION CASE 7.2 Text Mining Improves Hong Kong
Governments Ability to Anticipate and Address Public Complaints
Text Mining Applications
Marketing Applications
Security Applications
~
295
300
301
301
APPLICATION CASE 7.3 Mining for Lies
Biomedical App ...
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One of the first conflicts that would need to be investigated would be whether the human service professional followed the responsibility to client ethical standard. While developing a relationship with client it is important to clarify that if danger or
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4. A U.S. Supreme Court case known as Furman v. Georgia (1972) is a landmark case that involved Eighth Amendment’s ban of unusual and cruel punishment in death penalty cases (Furman v. Georgia (1972)
With covid coming into place
In my opinion
with
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The ability to view ourselves from an unbiased perspective allows us to critically assess our personal strengths and weaknesses. This is an important step in the process of finding the right resources for our personal learning style. Ego and pride can be
· By Day 1 of this week
While you must form your answers to the questions below from our assigned reading material
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5 The family dynamic is awkward at first since the most outgoing and straight forward person in the family in Linda
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The most important benefit of my statistical analysis would be the accuracy with which I interpret the data. The greatest obstacle
From a similar but larger point of view
4 In order to get the entire family to come back for another session I would suggest coming in on a day the restaurant is not open
When seeking to identify a patient’s health condition
After viewing the you tube videos on prayer
Your paper must be at least two pages in length (not counting the title and reference pages)
The word assimilate is negative to me. I believe everyone should learn about a country that they are going to live in. It doesnt mean that they have to believe that everything in America is better than where they came from. It means that they care enough
Data collection
Single Subject Chris is a social worker in a geriatric case management program located in a midsize Northeastern town. She has an MSW and is part of a team of case managers that likes to continuously improve on its practice. The team is currently using an
I would start off with Linda on repeating her options for the child and going over what she is feeling with each option. I would want to find out what she is afraid of. I would avoid asking her any “why” questions because I want her to be in the here an
Summarize the advantages and disadvantages of using an Internet site as means of collecting data for psychological research (Comp 2.1) 25.0\% Summarization of the advantages and disadvantages of using an Internet site as means of collecting data for psych
Identify the type of research used in a chosen study
Compose a 1
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effect relationship becomes more difficult—as the researcher cannot enact total control of another person even in an experimental environment. Social workers serve clients in highly complex real-world environments. Clients often implement recommended inte
I think knowing more about you will allow you to be able to choose the right resources
Be 4 pages in length
soft MB-920 dumps review and documentation and high-quality listing pdf MB-920 braindumps also recommended and approved by Microsoft experts. The practical test
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One thing you will need to do in college is learn how to find and use references. References support your ideas. College-level work must be supported by research. You are expected to do that for this paper. You will research
Elaborate on any potential confounds or ethical concerns while participating in the psychological study 20.0\% Elaboration on any potential confounds or ethical concerns while participating in the psychological study is missing. Elaboration on any potenti
3 The first thing I would do in the family’s first session is develop a genogram of the family to get an idea of all the individuals who play a major role in Linda’s life. After establishing where each member is in relation to the family
A Health in All Policies approach
Note: The requirements outlined below correspond to the grading criteria in the scoring guide. At a minimum
Chen
Read Connecting Communities and Complexity: A Case Study in Creating the Conditions for Transformational Change
Read Reflections on Cultural Humility
Read A Basic Guide to ABCD Community Organizing
Use the bolded black section and sub-section titles below to organize your paper. For each section
Losinski forwarded the article on a priority basis to Mary Scott
Losinksi wanted details on use of the ED at CGH. He asked the administrative resident